Ensemble Learning: Stacking, Blending & Voting

Description

Summary

• Stacking, Blending & Voting If you want to increase the effectiveness of your ML model, maybe you should consider Ensemble Learning We have heard the phrase “unity is strength”, whose meaning can be transferred to different areas of life.
• Ensemble Learning performs a strategic combination of various experts or ML models in order to improve the effectiveness obtained using a single weak model [1, 2].
• As we can see, in line 19 we are receiving the predictions of k-fold cross validation and in line 26 we are “stacking” these predictions (the which are forming the training data of the meta-model).
• In line 4 we are defining the 5 base classifiers that we will use (weak learners), in line 11 we define the final classifier, as in the previous example, we will use Logistic Regression.